Bibliographic record
Abstract
This book discusses smart city implementation in 11 smart cities - Auckland, Boston, Copenhagen, Gothenburg, Guangzhou, Hangzhou, Melbourne, Milan, Seoul, Tokyo, and Vancouver. The cities encompass a range of smart city development on selected critical issues in economic prosperity (future digital economy, smart retail, smart tourism), social inclusion (digital inclusion, digital placemaking, smart health service, smart youth empowerment), and environmental sustainability (climate resilience action, circular economy, smart climate action). The focus is on their challenges and course of action in and around the socio-technical systems and processes of sustainability transition. The chapters focus on emerging issues, enabling technologies, practical approaches, policies and case studies. The analysis recognises that smart city development takes place in a social context that, to some degree, will influence the adoption and effectiveness of technologies and ultimately, determine whether they meet end-user satisfaction. Smart city development is pivoted on technological changes, connectivity, and data, but also on people and government involvement and the transformation of urban living practices and conditions. This book aims to deepen dialogues on possible smart city strategies from the perspective of how people, organisations (e.g., processes, communication networks), and technologies interact to achieve individual, organisational, or societal goals
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.167 | 0.098 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".